Static Regret

Static regret, a measure of an algorithm's cumulative performance compared to a fixed optimal strategy in hindsight, is a key metric in online learning and decision-making problems. Current research focuses on extending static regret analysis to more dynamic settings, developing algorithms with improved regret bounds (often logarithmic in time horizon or problem size), and applying these concepts to diverse applications like caching, reinforcement learning, and human-robot interaction. This work aims to create more robust and adaptable algorithms for non-stationary environments, improving efficiency and performance in real-world scenarios where conditions change over time.